Introduction
Recent disasters around the world have shown clearly that not all threats can be averted. Modern societies are trying to enhance their resilience against extreme events after realizing that they can- not prevent every risk from being realized, but rather they must manage risks and adapt minimizing the impact on the population and their support systems.
Resilience, according to the dictionary, means ‘‘the ability to recover from (or to resist being affected by) some shock, insult or disturbance’’ and the root of the term has to be found in the Latin word ‘resilient’ literary means ‘to jump back’. Manyena SB. (2006), evaluating all the possible definitions provided from the 90s to nowadays, suggests that Resilience could be viewed as the ‘‘intrinsic capacity of a system, community or society predisposed to a shock or stress to adapt and survive by changing its non-essential attributes and rebuilding itself’’.
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Problem Statement
- After the earthquake (8 October 2005) different studies were carried out which only emphasized the risk & vulnerability assessment.
- There is a need to include parameters like time or recovery time and resources with loss estimation models to obtain a new model (Resilience Modto helping in the decision or policy-making process.
- Quantifying the resilience of infrastructures is a step toward developing smart cities. We can measure the functionality of the city before a disaster, predict the losses, and estimate the recovery time in terms of available resources.
- A proper resilience framework is needed for the local communities of Pakistan.
Literature survey with references
Davidson and Cagnan (2004) developed a model of the post-earthquake restoration processes for an electric power system. A discrete event simulation model based on available data was built, to improve the quantitative estimates of restoration times that are required to evaluate economic losses and identify ways to improve the restoration processes in future earthquakes.
Chang and Shinozuka (2004) contribute to the literature on disaster resilience discussing a quantitative measure of resilience based on the case study of the Memphis water system. They explored the extent to which loss estimation models can be used to measure resilience.
Cimellaro et al., (2005), attempted to formulate the first framework to quantify resilience, however only the uncertainties of the intensity measure I were considered, whereas in the framework proposed in this work, all other uncertainties are involved.
Miles and Chang (2006) present a comprehensive conceptual model of recovery, which establishes the relationships among a community’s household business, lifeline networks, and neighborhoods. The primary aim is to discuss issues of community recovery and to attempt to operationalize it. Even if a measure of resilience is not provided in their work, the paper points out the necessity to correlate the concept of recovery to real factors, such as the household object, whose attributes are the income, the year the building of residence was built, and the possible existence of any retrofit building.
Bruneau and Reinhorn (2007) for the first time relate probability functions, fragilities, and resilience in a single integrated approach for acute care facilities. After having defined the main properties and concepts of resilience, two different options to quantify the disaster resilience of acute care facilities are exposed as the percentage of healthy population and as the number of patients/day that can receive service.
Zobel, C.W. (2010) defines two of the primary measures that characterize the concept of disaster resilience are the initial impact of a disaster event and the subsequent time to recovery. He presented a new analytic approach to representing the relationship between these two characteristics by extending a multi-dimensional approach for predicting resilience into a technique for fitting the resilience function to the preferences and priorities of a given decision maker.
Shahzada et al., (2011) presented the hazard assessment of Abbottabad City based on an experimental study. The research focuses primarily on the Multi-Channel Analysis of Surface Waves (MASW) and Standard Penetration Test (SPT) results in different locations of the city. A correlation has been developed between shear wave velocity (Vs) determined through MASW tests and SPT Count Number (N) for soft clays. DEEPSOIL software has been used for the site response analysis based on the October 8, 2005 earthquake record at Tarbela. NEES Integrated Seismic Risk Assessment Framework (NISRAF) software has been used for the development of a hazard map for Abbottabad City. N. N. Ambraseys attenuation relationship for subduction zone has been used in this study. The surface strata based on the shear wave velocity and SPT count number in Supply-Kakul, Main City, and Mandian are soft whereas in Nawansher and the surrounding area is vary from soft to hard. Peak ground acceleration has been given in the form of a contour map.
Ahmad et al., (2014) have presented models for physical damageability assessment and socioeconomic loss estimation of structures in Pakistan for earthquake-induced ground motions, derived using earthquake loss estimation methodologies.
The goal of this research has been to provide a framework for a quantitative definition of resilience using an analytical function that may fit both technical and organizational issues.
References
- Cimellaro, G. P., Reinhorn, A. M., & Bruneau, M. (2010). Framework for analytical quantification of disaster resilience. Engineering Structures, 32(11), 3639–3649.
- Crowley, H., Ahmad, N., Ali, Q., & Pinho, R. (2014). Earthquake loss estimation of residential buildings in Pakistan, (September).
- Cimellaro, G. P., Renschler, C., Reinhorn, A. M., & Arendt, L. (2016). PEOPLES: A Framework for Evaluating Resilience. Journal of Structural Engineering, 142(10), 04016063.
- Shahzada, K., Gencturk, B., Khan, A.N., Naseer, A., Javed, M., & Fahad, M. (2014). Vulnerability Assessment of Typical Buildings in Pakistan Vulnerability Assessment of Typical Buildings in Pakistan, (January 2011).
- Christopher W. Zobel “Representing perceived tradeoffs in defining disaster resilience”
- Ahmad, N. (2014). Development of a seismic risk/loss model for Mansehra City, Pakistan Development of a Seismic Risk / Loss Model For Mansehra City, Pakistan (June).
- Barberis, F., & Cimellaro, G. P. (2018). Fragility Curves of Restoration Processes for Resilience Analysis, (July).
- Cimellaro, G. P. (2013). Resilience-based design (RBD) modeling of civil infrastructure to assess seismic hazards. Handbook of seismic risk analysis and management of civil infrastructure systems.
- Cimellaro, G. P., Reinhorn, A. M., & Bruneau, M. (2006). Quantification of Seismic Resilience. 8th U.S. National Conference on Earthquake Engineering, (1094).
- Hosseini, S., Barker, K., & Ramirez-Marquez, J. E. (2016). A review of definitions and measures of system resilience. Reliability Engineering and System Safety, 145, 47–61.
- Kanto, G. (2013). Earthquake Resilience of High-Rise Buildings : Case Study of the 2011 Tohoku (Japan) Earthquake.
- Porter, K., Eeri, M., Kennedy, R., Eeri, M., Bachman, R., & Eeri, M. (2007). Creating Fragility Functions for Performance-Based Earthquake Engineering BACKGROUND AND OBJECTIVES, 23(2), 471–489.
- Shahzada, K. (2011). Seismic risk assessment of buildings in Pakistan (case study Abbottabad city). Thesis.
- Zobel, C. W. (2014). Quantitatively Representing Nonlinear Disaster Recovery ∗. Decision Sciences Institute, 45(6), 1053–1082.
- Zobel, C. W. (2010). Comparative Visualization of Predicted Disaster Resilience. Seventh International ISCRAM Conference, (May), 1–6.
- Zobel, C. W. (2013). Analytically comparing disaster recovery following the 2012 derecho. ISCRAM 2013 Conference Proceedings, (May), 678–682.
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